Even from the first page, I received the impression that “Complexity theory in the study of space and place” by Manson and O’Sullivan (2006) was a well-written paper. It tells a story and proceeds at a smooth pace that is easy to read, while still providing substantial information on the topic. I did find the constant references to various philosophical theories, such as reductionism and holism, difficult to assimilate into my understanding of complexity as I do not have a background in such theories. I felt like I was receiving an introduction to philosophy and complexity at the same time – a bit overwhelming! However, it did make me realize that an understanding of basic philosophical theories would probably help my conceptualization of GIScience as a whole – which was not a connection I thought to make in this class. To give credit where it is due, the authors did help comprehension by providing short definitions or context for obscure words within the text.
When asking the three main questions of “(1) Does complexity theory operate at too general a level to enhance understanding? (2) What are the ontological and epistemological implications of complexity? And (3) What are the challenges in modeling complexity? (678)” the paper highlights the tension inherent in the field of complexity. One problem that seemed especially prominent was the conflict between understanding emergent behaviour and the desire to simplify models. Computational modelling was provided as both a solution to accommodating large amounts of heterogeneous variables while also being presented as an easy avenue towards simplification (683).
The authors also made some references to spatial scale that I found particularly intriguing – namely how emergence and scaling up from local to more global phenomena can conflict with modeling assumptions of uniform patterns over different scales. I am finding more and more that all of our individual research topics are converging on each other. Complexity relates to spatial scale, which relates to ontologies, which relates to uncertainty, and so forth. I have not yet fully decided what that means for the broader context of understanding GIScience in my own head but I think it is important to acknowledge the increasingly common ground. I feel as if, through this class, I am step by step building my own conceptual network model of GIScience. It is not a linear path by any means – rather circular and backtracking in fact – but slowly, slowly, slowly the connections form.
-Vdev